Top Data & Analytics Jobs

Research and analyze competitors' annuity products, pricing, sales, and trends. Maintain competitor databases and produce regular reports and rate updates. Partner with internal teams, present findings to stakeholders, mentor junior analysts, and conduct requested research projects to inform product and actuarial decisions.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Design and build production-grade data pipelines on Databricks and AWS extracting from enterprise ERPs, implement medallion architectures, ensure data quality/governance, collaborate with business SMEs, and deliver AI-ready data products.

Develop, deploy, and maintain Oracle-based reporting solutions and dashboards (OAC, OTBI, FDI). Manage integrations and data migrations for Oracle Financials Cloud, perform system maintenance and performance tuning, build reporting strategy and documentation, partner with finance stakeholders to gather requirements, train users, ensure compliance and controls, and support analytics projects, upgrades, and issue resolution.

Partner with marketing and analytics teams to analyze campaign and funnel performance, deliver AI-driven insights, build real-time Power BI dashboards, ensure data quality and governance, and provide actionable recommendations to drive revenue and optimization.

Operates and improves enterprise data and analytics platforms by building, monitoring, troubleshooting, and optimizing ETL/ELT pipelines, integrations, datasets, semantic layers, and reporting solutions. Performs SQL and Python analysis, data validation, reconciliation, root-cause analysis, incident resolution, platform administration, and automation. Supports Databricks, Snowflake, Azure, SAP, Power BI, and MicroStrategy environments while advancing data quality, governance, security, lineage, reliability, and AI/ML initiatives.

Develop and support Informatica MDM and IDMC solutions across customer, supplier, product, material, and finance domains. Design master data configurations, integration pipelines, data quality rules, mappings, and governance workflows. Integrate MDM with SAP S/4HANA, ERP, cloud, and legacy systems while supporting cloud modernization, production troubleshooting, deployments, testing, and CI/CD. Collaborate with data governance teams, business stakeholders, and Agile engineering teams to deliver scalable, accurate, and compliant enterprise data solutions.

Designs and maintains scalable data pipelines using Databricks, Spark, and cloud services. Builds transformation workflows, integrates APIs, databases, and cloud storage, and supports analytics and reporting. Implements data validation, monitoring, observability, governance, security, and automation. Collaborates with data science, analytics, and business teams to deliver reliable data solutions in AWS and other cloud environments.

Owns revenue analytics infrastructure, including dashboards, tracking systems, forecasting models, and performance evaluation for RPD/RPU, utilization, and segment targets. Monitors macroeconomic and industry trends, supports pricing and revenue strategy, maintains the data stack, and delivers executive-ready recommendations. The role also manages a small analyst team and supports go-to-market measurement and post-launch evaluation.

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.